Boris Shpak

Abstract Gene set enrichment analysis (GSEA) is an ubiquitously used tool for evaluating pathway enrichment in transcriptional data. Typical experimental design consists in comparing two conditions with several replicates using a differential gene expression test followed by preranked GSEA performed against a collection of hundreds and thousands of pathways. However, the reference implementation of this method cannot accurately estimate small P-values, which significantly limits its sensitivity due to multiple hypotheses correction procedure. Here we present FGSEA (Fast Gene Set Enrichment Analysis) method that is able to estimate arbitrarily low GSEA P-values with a high accuracy in a matter of minutes or even seconds. To confirm the accuracy of the method, we also developed an exact algorithm for GSEA P-values calculation for integer gene-level statistics. Using the exact algorithm as a reference we show that FGSEA is able to routinely estimate P-values up to 10 −100 with a small and predictable estimation error. We systematically evaluate FGSEA on a collection of 605 datasets and show that FGSEA recovers much more statistically significant pathways compared to other implementations. FGSEA is open source and available as an R package in Bioconductor ( http://bioconductor.org/packages/fgsea/ ) and on GitHub ( https://github.com/ctlab/fgsea/ ).

The lack of comprehensive diagnostics and consensus analytical models for evaluating the status of a patient's immune system has hindered a wider adoption of immunoprofiling for treatment monitoring and response prediction in cancer patients. To address this unmet need, we developed an immunoprofiling platform that uses multiparameter flow cytometry to characterize immune cell heterogeneity in the peripheral blood of healthy donors and patients with advanced cancers. Using unsupervised clustering, we identified five immunotypes with unique distributions of different cell types and gene expression profiles. An independent analysis of 17,800 open-source transcriptomes with the same approach corroborated these findings. Continuous immunotype-based signature scores were developed to correlate systemic immunity with patient responses to different cancer treatments, including immunotherapy, prognostically and predictively. Our approach and findings illustrate the potential utility of a simple blood test as a flexible tool for stratifying cancer patients into therapy response groups based on systemic immunoprofiling.

A broth enrichment medium for the improvement of isolation of Campylobacter jejuni-Campylobacter coli from stool samples and other specimens is presented. Of 1,228 samples examined in parallel, positive results were obtained from 81 by direct inoculation of selective media and from 112 after enrichment. Thus, an increase of 27.7% in the isolation rate was obtained by using the enrichment medium. The same medium without antibiotics allows the preservation of isolates of C. jejuni-C. coli for at least 2 months at 4 degrees C.

Here, we show that Porphyromonas gingivalis ( Pg ), an endogenous oral pathogen, dampens all aspects of interferon (IFN) signaling in a manner that is strikingly similar to IFN suppression employed by multiple viral pathogens. Pg suppressed IFN production by down-regulating several IFN regulatory factors (IRFs 1, 3, 7, and 9), proteolytically degrading STAT1 and suppressing the nuclear translocation of the ISGF3 complex, resulting in profound and systemic repression of multiple interferon-stimulated genes. Pg -induced IFN paralysis was not limited to murine models but was also observed in the oral tissues of human periodontal disease patients, where overabundance of Pg correlated with suppressed IFN generation. Mechanistically, multiple virulence factors and secreted proteases produced by Pg transcriptionally suppressed IFN promoters and also cleaved IFN receptors, making cells refractory to exogenous IFN and inducing a state of broad IFN paralysis. Thus, our data show a bacterial pathogen with equivalence to viruses in the down-regulation of host IFN signaling.